From engineer to product leader
Vibha Choudhary is an engineer-turned-product leader with more than 15 years of experience building AI and ML products. She began her career as a senior software engineer in fintech before transitioning into product management and taking on AI and ML leadership roles at Expedia, Amazon, Microsoft, and Atlassian. That combination of hands-on engineering experience and product stewardship has defined a career spent translating research and algorithms into products used at global scale. Her technical foundation informs a pragmatic product approach: she repeatedly shepherds complex capabilities from prototype and research into robust, user-facing features.
Building at the frontier of AI and ML
Long before the recent generative AI surge, Choudhary was already building production-grade machine-learning systems. At Expedia she worked on an ML-powered pricing platform, applying data-driven techniques to a business-critical optimization problem and helping the company operationalize models that influence customer pricing and inventory decisions. Her move to Amazon marked a deeper focus on language technologies. As a Senior Product Manager she worked on natural-language understanding, natural-language generation and conversational AI for Alexa, helping refine how machines interpret and generate human language in interactive settings and improving the practical reliability of conversational experiences.
At Microsoft Choudhary continued to build generative AI experiences at scale. She served as a Principal Product Manager and contributed to products such as Microsoft Designer, applying generative models to creative workflows. She also co-authored a white paper on generative AI capabilities and their application to new product experiences, translating technical capability into product strategy and design. As part of the Microsoft Copilot effort, she led work on Copilot Notebooks — an AI-native workspace designed to reason across a persistent collection of user-provided sources — demonstrating a consistent interest in combining large models with structured user context to create practical, context-aware tools.
Leading AI creation for Confluence
Choudhary is currently a Principal Product Manager at Atlassian, where she leads AI creation experiences for Confluence. Her work addresses the familiar ‘blank-page’ problem by bringing together generative AI with enterprise context: helping users create, organize, and develop their teams’ knowledge faster and more reliably. She leads the strategy, development, and execution of AI-powered creation and agentic capabilities designed to help people move work forward more effectively, applying product thinking to ensure AI augments knowledge work rather than complicating it. She drove Create with Rovo from concept to launch, and led exploratory work on conversational AI and voice AI capabilities.
Intellectual contributions and real-world impact
Across her career, Choudhary has repeatedly worked at the intersection of emerging technology and product delivery. Her experience spans machine learning, conversational AI, generative AI, agentic systems, AI governance and human–computer interaction. She is also an inventor with multiple patents — a marker of sustained, practical contributions that bridge research and implementation. Those patents and product efforts reflect an orientation toward solving engineering challenges that matter to end users and organizations at scale.
Mentorship, community and competitive engineering
Beyond product leadership, Choudhary is an active speaker, mentor, hackathon judge and contributor to the technology community. She has presented at events including an AI and security session featured at the RSA Conference and has been a featured speaker at Product School, among other industry gatherings. She serves as a mentor and selection-panel member for TechWomen, supporting emerging women leaders in STEM, and has contributed to the University of Washington’s product management program.
Choudhary also participates in competitive and community-driven engineering events. She judged a Conversational AI Hackathon hosted at Y Combinator, where a team she shortlisted went on to place third in the final judging. These hands-on roles — mentor, judge and advisor — illustrate a commitment to nurturing talent, helping teams navigate early-stage product decisions, and promoting practical innovation beyond corporate walls.
Exploring quantum approaches to AI
Choudhary’s intellectual curiosity extends beyond classical computing. After independently studying quantum computing, she participated in a Quantum AI hackathon where her team won first prize. The project, TeraQub, explored how quantum hardware characteristics, materials, error-correction codes and decoders can be matched more effectively. Following the hackathon she began collaborating with a quantum physicist from MIT to further the research, exploring ways to reduce physical-qubit overhead, lower costs and make useful quantum computing more accessible. That work underscores a willingness to apply product and systems thinking to frontier research problems.
A career that connects engineering, product and impact
Vibha Choudhary’s trajectory — from engineering in fintech to shaping generative and conversational AI at some of the world’s largest technology companies — illustrates a rare blend of technical depth and product leadership. Whether developing pricing platforms, refining language understanding for voice assistants, building AI-native workspaces, mentoring the next generation of technologists, or exploring quantum approaches to computation, her work centers on turning complex capabilities into useful, scalable products that help people and teams do better work.
What to watch next
Choudhary continues to lead product efforts that integrate large models with enterprise context, while also advancing research that probes the edges of what future computing platforms might enable. Her combination of practical product experience, technical inventiveness and community engagement makes her a figure to watch as AI products evolve from experimental features into core tools for knowledge work.
